Method for rejecting tuning disturbances to improve lamp failure prediction quality in thermal processes
AssigneeApplied Materials, Inc.
InventorsSubrahmanyam Venkata Rama Kommisetti, Haw Jyue Luo, Jimmy Iskandar, Hsincheng Lai, Parris Hawkins
Life of this patent
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Abstract
Embodiments disclosed herein include methods for reducing or eliminating the impact of tuning disturbances during prediction of lamp failure. In one embodiment, the method comprises monitoring data of a lamp module for a process chamber using one or more physical sensors disposed at different locations within the lamp module, creating virtual sensors based on monitoring data of the lamp module, and providing a prediction model for the lamp module using the virtual sensors as inputs.
Claims9
A method for lamp failure prediction, comprising:
- creating virtual sensors by collecting data comprising current and resistance related to an upper lamp and a lower lamp of a lamp module disposed in a semiconductor process chamber, wherein the virtual sensors are represented as equations selected from one or more of the following:
- first
-
-
- virtual
-
-
- sensor
- =
- log
-
-
- (
- abs
-
- (
- TII
- k
-
-
- 1
- *
- TIR
- )
- +
- 1
- )
- ,
-
- second
-
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- TOI
- k
-
-
- 2
- *
- TOR
- )
- +
- 1
- )
- ,
-
- third
-
-
- virtual
-
-
- sensor
- =
- log
-
-
- (
- abs
-
-
- (
- BII
- k
-
-
- 3
- *
- BIR
- )
- +
- 1
- )
- ,
-
- fourth
-
-
- virtual
-
-
- sensor
- =
- log
-
-
- (
- abs
-
- (
- BOI
- k
-
-
- 4
- *
- BOR
- )
- +
- 1
- )
- ,
-
- and
- fifth
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- TII
- k
-
-
- 1
- *
- TIR
- +
- TOI
- k
-
-
- 2
- *
- TOR
- +
- BII
- k
-
-
- 3
- *
- BIR
- +
- BOI
- k
-
-
- 4
- *
- BOR
- )
- +
- 1
- )
- ,
- wherein TII is “Top Inner Current,” BII is “Bottom Inner Current,” TOI is “Top Outer Current,” BOI is “Bottom Outer Current,” TIR is “Top Inner Resistance,” BIR is “Bottom Inner Resistance,” TOR is “Top Outer Resistance,” BOR is “Bottom Outer Resistance,” and the expression “log” in equations is natural logarithm, and where factors k1, k2, k3, k4 are constants extracted from the data;
- providing a lamp failure prediction for the lamp module using the virtual sensors as inputs;
- and
- performing maintenance on the lamp module based on the lamp failure prediction to increase uptime and reduce unscheduled downtime.
A method, comprising:
- collecting data of a lamp module disposed in a semiconductor process chamber from one or more physical sensors disposed at different locations within the lamp module;
- creating virtual sensors based on the data of the lamp module, wherein the virtual sensors are represented as equations selected from one or more of the following:
- first
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- TII
- k
-
-
- 1
- *
- TIR
- )
- +
- 1
- )
- ,
-
- second
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- TOI
- k
-
-
- 2
- *
- TOR
- )
- +
- 1
- )
- ,
-
- third
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- BII
- k
-
-
- 3
- *
- BIR
- )
- +
- 1
- )
- ,
-
- fourth
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- BOI
- k
-
-
- 4
- *
- BOR
- )
- +
- 1
- )
- ,
- and
- fifth
-
-
- virtual
-
-
- sensor
- =
- log
-
- (
- abs
-
- (
- TII
- k
-
-
- 1
- *
- TIR
- +
- TOI
- k
-
-
- 2
- *
- TOR
- +
- BII
- k
-
-
- 3
- *
- BIR
- +
- BOI
- k
-
-
- 4
- *
- BOR
- )
- +
- 1
- )
- ,
- wherein TII is “Top Inner Current,” BII is “Bottom Inner Current,” TOI is “Top Outer Current,” BOI is “Bottom Outer Current,” TIR is “Top Inner Resistance,” BIR is “Bottom Inner Resistance,” TOR is “Top Outer Resistance,” BOR is “Bottom Outer Resistance,” and the expression “log” in equations is natural logarithm, and where factors k1, k2, k3, k4 are constants extracted from the data;
- providing a prediction model for the lamp module using the virtual sensors as inputs;
- and
- performing maintenance on the lamp module based on the prediction model to increase uptime and reduce unscheduled downtime.
Dependencies are inferred from the claim text.